Vision-Language Models (VLMs) have transformed tasks requiring visual and reasoning abilities, such as image retrieval and Visual Question Answering (VQA). Despite their success, VLMs face significant challenges with tasks involving geometric reasoning, algebraic problem-solving, and counting. These limitations stem from difficulties effectively integrating multiple modalities and accurately interpreting geometry-related tasks. Various works claim that introducing a captioning pipeline before VQA tasks enhances performance. We incorporated this pipeline for tasks involving geometry, algebra, and counting. We found that captioning results are not generalizable, specifically with larger VLMs primarily trained on downstream QnA tasks showing random performance on math-related challenges. However, we present a promising alternative: task-based prompting, enriching the prompt with task-specific guidance. This approach shows promise and proves more effective than direct captioning methods for math-heavy problems.
We propose a novel approach for dynamic negative prompting in diffusion models that leverages Vision-Language Models (VLMs) to adaptively generate negative prompts during the denoising process. Unlike traditional Negative Prompting methods that use fixed negative prompts, our method generates intermediate image predictions at specific denoising steps and queries a VLM to produce contextually appropriate negative prompts. We evaluate our approach on various benchmark datasets and demonstrate the trade-offs between negative guidance strength and text-image alignment.
RACap: Relation-Aware Prompting for Lightweight Retrieval-Augmented Image Captioning
School of biological science and medical engineering, Southeast University, China（生物科学与医学工程学院，东南大学，中国）；Department of Neurosurgery, The second affiliated hospital of Xinjiang Medical University, China（神经外科部门，新疆医科大学第二附属医院，中国）
CapArena: Benchmarking and Analyzing Detailed Image Captioning in the LLM Era
National Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；The University of Hong Kong（香港大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Despite the remarkable progress of image captioning, existing captioners typically lack the controllable capability to generate desired image captions, e.g., describing the image in a rough or detailed manner, in a factual or emotional view, etc. In this paper, we show that a unified model is qualified to perform well in diverse domains and freely switch among multiple styles. Such a controllable capability is achieved by embedding the prompt learning into the image captioning framework. To be specific, we design a set of prompts to fine-tune the pre-trained image captioner. These prompts allo…
Evaluating the Robustness of Open-Source Vision-Language Models to Domain Shift in Object Captioning
Centre for Robotic Autonomy in Demanding and Long-lasting Environments（机器人自主性在恶劣和持久环境中的研究中心）；Centre for Robotics and AI（机器人与人工智能中心）；University of Manchester（曼彻斯特大学）；Amentum Clean Energy Ltd（Amentum清洁能源有限公司）
Transferable Decoding with Visual Entities for Zero-Shot Image Captioning
Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Tencent（腾讯）；Shanghai Jiao Tong University（上海交通大学）；Southern University of Science and Technology（南方科技大学）；The University of Hong Kong（香港大学）
PANORAMA: Panoptic Grounded Captioning via Mask Proposal Selection
Inria, École normale supérieure, CNRS, PSL Research University（法国国家信息与自动化研究所，巴黎高等师范学院，法国国家科学研究中心，巴黎文理研究大学）；Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague（捷克信息学、机器人与控制论研究所，布拉格捷克理工大学）
Mitigating Image Captioning Hallucinations in Vision-Language Models
Department of Computer Science, The University of Alabama at Birmingham（亚拉巴马大学伯明翰分校计算机科学系）；Department of Computer Science, University of Waterloo（滑铁卢大学计算机科学系）
Can Argus Judge Them All? Comparing VLMs Across Domains
Bharati Vidyapeeth（巴哈提大学）；Macquarie University（麦考瑞大学）；DSEU-Okhla；Vivekananda Institute of Professional Studies（维维kananda专业研究学院）；IIIT-Delhi（德里印度理工学院）；Center for SDGC（SDGC中心）；Stanford University（斯坦福大学）
Michigan State University（密歇根州立大学）；University of Michigan（密歇根大学）
This work explores the zero-shot compositional learning ability of large pre-trained vision-language models(VLMs) within the prompt-based learning framework and propose a model (\textit{PromptCompVL}) to solve the compositonal zero-shot learning (CZSL) problem. \textit{PromptCompVL} makes two design choices: first, it uses a soft-prompting instead of hard-prompting to inject learnable parameters to reprogram VLMs for compositional learning. Second, to address the compositional challenge, it uses the soft-embedding layer to learn primitive concepts in different combinations. By combining both s…
